For the best of my knowledge, my project is the first RDN implementation written by Keras. However, I still need to acknowledge that https://github.com/yulunzhang/RDN and https://github.com/hengchuan/RDN-TensorFlow help me for some hyperparameter setting.
This file generates our training datasets for three different degradations, including 5 datasets.
- Bicubic degradation with factor 2, 3, and 4.
- BD degradation with factor 3
- DN degradation with factor 3 Also this file generates 25 datasets for 5 degradations on 5 benchmark datasets, which have not been seen while training. To generate those dataset, I wrote function to chop, rescale, filter, read, and save the image.
I implement this project by Keras. I utilize a standard Keras framework with a data_loader and model file.
This file is used for load the data from the dataset for training, sampling and testing.
This file include the entire RDN network. Also, it can test on validation set while traing and load a trained model for testing.
RDB_run.sh is used to train in cluster by qsub.
Since we need to calculate the metric, and online calculate takes too long, I first generate the entire reconstructed high resolution dataset for metric calculation. This result dataset include ground truth and reconstructed image.
Calculate the Metric among all 25 testing datasets. Result is in the Metric.log, which is consistent with the result displayed in the term paper.
Plot loss from training log file.
Generate an entire high resolution image from reconstructed patches.
https://drive.google.com/open?id=1sQZO1ZZ4MIYM_vF5wOm6e9F8OiyeoF_H
- RDB_best_16.h5, BI degradation, factor 3
- RDB_best_17.h5, BI degradation, factor 2
- RDB_best_18.h5, BI degradation, factor 4
- RDB_best_19.h5, BD degradation, factor 3
- RDB_best_20.h5, DN degradation, factor 3